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Survival Analysis for Epidemiologic and Medical Research / Edition 1

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Overview

This practical guide shows why the analytic methods work and how to effectively analyze and interpret epidemiologic and medical survival data with the help of modern computer systems. The introduction presents a review of a variety of statistical methods that are not only key elements of survival analysis but are also central to statistical analysis in general. Techniques such as statistical tests, transformations, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to understanding the analysis of many kinds of data. Similarly, discussions of such statistical concepts such as bias, confounding, independence, and interaction are presented in the context of survival analysis as well as the basic components of a broad range of applications.

About the Author

Steve Selvin is Professor of Biostatistics and Epidemiology at the University of California, Berkeley. He has taught on the Berkeley campus for 35 years and has authored or co-authored more than 200 scientific articles in the areas of applied statistics and epidemiology. He has received two university teaching awards and is a member of the ASPH/Pfizer Public Health Academy of Distinguished Teachers.

Editorial Reviews

'... for students who know very little about statistics and mathematics this book is a welcome resource on the techniques used to analyse time-to-event data. These techniques are introduced in a non-technical way accompanied with many good examples. Also the included R-code gives a good introduction to the practical analysis.' Biometrical Journal

'This book provides an easy-to-read introduction to the fundamental concepts applicable to survival analysis without relying on mathematical prerequisites. ... [the] text gives a thorough introduction to the area of survival analysis for those with little prior statistical knowledge.' International Journal of Epidemiology